Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Investigating production system representations for non-combinatorial match
Artificial Intelligence
Combining left and right unlinking for matching a large number of learned rules
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Rule based updates on simple knowledge bases
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Explanations in Knowledge Systems: Design for Explainable Expert Systems
IEEE Expert: Intelligent Systems and Their Applications
A Study of Explanation-Based Methods for Inductive Learning
Machine Learning
Parameter network as a means for driving problem solving process
International Journal of Computer Applications in Technology
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This paper presents a method to reorganize rules in knowledge bases with the objective of improving their performance. Knowledge reorganization is achieved through the combination of rule compression and abstraction techniques. The effectiveness of this methodology is evaluated in terms of pattern matching activity and execution times using knowledge bases from several application areas.